The goal of this project is to overcome the currently unsolved problem of using magnetic resonance imaging (MRI) to measure temperature changes in fat-based tissues. Thermal therapies that use minimally invasive and non-invasive techniques such as radio frequency currents, microwaves, or high intensity focused ultrasound (HIFU) have the potential to revolutionize tumor ablation and drug delivery procedures. Due to the minimally invasive nature of these techniques, constant monitoring of energy deposition and temperature changes are required to ensure treatment efficacy and patient safety. Many investigators have chosen to perform these procedures under MRI guidance because of the improved contrast in soft tissue imaging, the elimination of ionizing radiation, and the ability to measure real time temperature changes in water-based tissues. However, the ability to use MRI for fast, accurate, and robust temperature measurements in fat-based tissues remains an unsolved problem. This limitation represents a large obstacle to the implementation of non- invasive thermal therapies in sites where thermal energy may be deposited in fatty tissue. MR thermometry techniques based on the temperature dependence of the water proton resonant frequency (PRF) are well established and in wide use for water-based tissues, however the method is ineffective in fat- based tissues. To measure temperature changes in fat, investigators have turned to the longitudinal relaxation time, T1, which is temperature dependent for both water- and fat-based tissues. Unfortunately, T1 based temperature measurements are significantly less accurate and stable than PRF temperature measurements in water-based tissues. In light of these challenges, we are taking a two pronged approach to solving the problem of MR temperature measurements in fat. First, the MR sequence will be designed to simultaneously acquire PRF and T1 information. This will allow PRF temperature measurements to be made in all water-based tissues, and T1 temperature measurements to be made in al fat-based tissues. Second, predictions from a thermal model will be incorporated into the process in order to improve the accuracy of the T1 temperature measurements in fat-based tissues. The project will be carried out in four steps. First, we will study the behavior of T1 as a function of temperature in a variety of tissue types. Second, we will develop and optimize the hybrid PRF/T1 sequence. Third, we develop thermal modeling techniques for inhomogeneous tissues. Fourth, we will test and optimize methods for combining the MR temperature measurements with the thermal model temperature predictions.